U.S. patent application number 15/855046 was filed with the patent office on 2018-06-28 for radiotherapy planning apparatus and clinical model comparison method.
This patent application is currently assigned to Toshiba Medical Systems Corporation. The applicant listed for this patent is Toshiba Medical Systems Corporation. Invention is credited to Kazuhisa Murakami, Longxun Piao, Kazuki Utsunomiya.
Application Number | 20180182495 15/855046 |
Document ID | / |
Family ID | 62630124 |
Filed Date | 2018-06-28 |
United States Patent
Application |
20180182495 |
Kind Code |
A1 |
Utsunomiya; Kazuki ; et
al. |
June 28, 2018 |
RADIOTHERAPY PLANNING APPARATUS AND CLINICAL MODEL COMPARISON
METHOD
Abstract
According to one embodiment, a radiotherapy planning apparatus
includes processing circuitry and a display. The processing
circuitry calculates, by applying patient information to each of a
plurality of analysis models relating to clinical practice,
analysis results based on the analysis models. The processing
circuitry compares each of the analysis results with an actual
clinical result relating to a comparison target patient, and
generates evaluation information to evaluate a change between the
analysis models. The display displays the evaluation
information.
Inventors: |
Utsunomiya; Kazuki;
(Nasushiobara, JP) ; Murakami; Kazuhisa; (Tsu,
JP) ; Piao; Longxun; (Nasushiobara, JP) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Toshiba Medical Systems Corporation |
Otawara-shi |
|
JP |
|
|
Assignee: |
Toshiba Medical Systems
Corporation
Otawara-shi
JP
|
Family ID: |
62630124 |
Appl. No.: |
15/855046 |
Filed: |
December 27, 2017 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16H 50/50 20180101;
G16H 20/40 20180101; G16H 50/70 20180101 |
International
Class: |
G16H 50/50 20060101
G16H050/50; G16H 50/70 20060101 G16H050/70 |
Foreign Application Data
Date |
Code |
Application Number |
Dec 28, 2016 |
JP |
2016-255330 |
Dec 26, 2017 |
JP |
2017-248638 |
Claims
1. A radiotherapy planning apparatus comprising: processing
circuitry that calculates, by applying patient information to each
of a plurality of analysis models relating to clinical practice,
analysis results based on the analysis models, compares each of the
analysis results with an actual clinical result relating to a
comparison target patient, and generates evaluation information to
evaluate a change between the analysis models; and a display that
displays the evaluation information.
2. The radiotherapy planning apparatus of claim 1, wherein the
processing circuitry applies the analysis models to each of pieces
of patient information of a plurality of patients, thereby
calculating the analysis results for each of the plurality of
patients.
3. The radiotherapy planning apparatus of claim 2, wherein the
processing circuitry sets the comparison target patient from the
plurality of patients.
4. The radiotherapy planning apparatus of claim 3, further
comprising a reference tendency information database that stores a
tendency of reference to medical information of the patient in a
medical treatment process relating to clinical practice for each
user, wherein the processing circuitry sets the comparison target
patient based on the tendency of reference.
5. The radiotherapy planning apparatus of claim 4, wherein the
processing circuitry acquires medical information which the user
refers to and a medical treatment process at a reference time, and
records the tendency of reference based on the acquired medical
information and medical treatment process.
6. The radiotherapy planning apparatus of claim 4, wherein: the
reference tendency information database stores, for at least one
item of a sex of a patient of a referred medical information, an
age of the patient, a treatment portion of the patient, and a type
of treatment apparatus used for the patient, a reference count for
each of the pieces of medical information of the plurality of
patients, and the processing circuitry sets, as the comparison
target patients, the patients of the pieces of medical information
each having the reference count of a predetermined one of the at
least one item, which is not smaller than a predetermined
count.
7. The radiotherapy planning apparatus of claim 6, wherein the
reference tendency information database stores the reference count
for each department or each hospital.
8. The radiotherapy planning apparatus of claim 3, wherein the
processing circuitry sets, as the comparison target patients, the
patients of the pieces of medical information referred to during a
latest predetermined period or the patients of the pieces of
medical information referred to during a specific past period.
9. The radiotherapy planning apparatus of claim 3, wherein if a
user designates one patient from the comparison target patients
shown in a chart, the display displays detailed information of the
designated patient.
10. The radiotherapy planning apparatus of claim 1, wherein: the
analysis models include a first analysis model and a second
analysis model obtained by updating the first analysis model; the
processing circuitry calculates a first analysis result by applying
the patient information to the first analysis model, and a second
analysis result by applying the patient information to the second
analysis model; and the evaluation information is evaluated based
on a comparison between the first analysis result and the actual
clinical result and a comparison between the second analysis result
and the actual clinical result.
11. The radiotherapy planning apparatus of claim 10, wherein the
evaluation information indicates a degree of improvement or
worsening of the second analysis model with reference to the first
analysis model, or indicates whether the second analysis model is
improved or worsened relative to the first analysis model.
12. The radiotherapy planning apparatus of claim 10, wherein the
processing circuitry calculates a clinical change between first
analysis result and the second analysis result with reference to
the actual clinical result for the comparison target patient.
13. The radiotherapy planning apparatus of claim 10, wherein: the
processing circuitry calculates a first accuracy index indicating
accuracy of the first analysis result based on the actual clinical
result, calculates a second accuracy index indicating accuracy of
the second analysis result based on the actual clinical result, and
calculates a comparison index indicating comparison between the
first accuracy index and the second accuracy index; and the
processing circuitry generates the evaluation information
schematically showing at least one of the first accuracy index, the
second accuracy index, or the comparison index for the comparison
target patient.
14. The radiotherapy planning apparatus of claim 13, wherein the
processing circuitry calculates, as the comparison index, a
difference between the first analysis result and the second
analysis result with reference to the actual clinical result, and a
degree of a clinical change of the second analysis result with
respect to the first analysis result.
15. The radiotherapy planning apparatus of claim 14, wherein the
evaluation information shows the differences for the comparison
target patients by visually discriminating the differences in
accordance with the degree of the clinical change.
16. The radiotherapy planning apparatus of claim 14, wherein the
display shows the differences of the comparison target patients in
descending order of the clinical change between the first analysis
result and the second analysis result.
17. The radiotherapy planning apparatus of claim 16, wherein the
display displays the evaluation information of a limited patient of
the comparison target patients, who has a degree of the clinical
change greater than a threshold.
18. The radiotherapy planning apparatus of claim 1, wherein: the
processing circuitry generates a verification screen showing a
tendency of the clinical change between the analysis models; and
the display displays the verification screen.
19. A clinical model comparison method comprising: calculating, by
applying patient information to each of a plurality of analysis
models relating to clinical practice, analysis results based on the
analysis models, comparing each of the analysis results with an
actual clinical result relating to a comparison target patient, and
generates evaluation information to evaluate a change between the
analysis models; and displaying the evaluation information.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is based upon and claims the benefit of
priority from the Japanese Patent Application No. 2016-255330,
filed Dec. 28, 2016, and the Japanese Patent Application No.
2017-248638, filed Dec. 26, 2017 the entire contents of both of
which are incorporated herein by reference.
FIELD
[0002] Embodiments described herein relate generally to a
radiotherapy planning apparatus and a clinical model comparison
method.
BACKGROUND
[0003] In cancer radiotherapy, it is said that treatment progress
such as the response (for example, the probability of cure after 3
months) of a cancer after irradiation and a side effect (for
example, weight loss) influences the QOL (Quality Of Life) of a
patient. Prediction of the treatment progress at the time of
radiotherapy planning can support decision making so that a doctor
makes a medical judgment (decides an action) effective for the QOL
of a patient. If, for example, it is predicted that a cancer will
not be reduced after 3 months, a doctor can make a medical judgment
to reconsider a treatment plan. If it is predicted that weight loss
will occur, a doctor can make a medical judgment to plan nutrition
intervention such as gastrostomy. There are provided many tools for
making it possible to construct/verify treatment progress
prediction models. To accurately evaluate these prediction models,
a user is required to be well versed in statistics.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] FIG. 1 is a block diagram showing the arrangement of a
radiotherapy planning apparatus according to an embodiment;
[0005] FIG. 2 is a table showing a basic model information table
managed in a mathematical model database shown in FIG. 1;
[0006] FIG. 3 is a table showing a detailed model information table
managed in the mathematical model database shown in FIG. 1;
[0007] FIG. 4 is a table showing a basic patient information table
managed in a patient information database shown in FIG. 1;
[0008] FIG. 5 is a table showing a treatment result information
table managed in the patient information database shown in FIG.
1;
[0009] FIG. 6 is a table showing a reference tendency information
table that indicates reference tendency information for each
patient and is managed in a reference tendency information
database;
[0010] FIG. 7 is a table that indicates reference tendency
information for each treatment portion and is managed in the
reference tendency information database;
[0011] FIG. 8 is a flowchart illustrating a model comparison
executed by processing circuitry shown in FIG. 1 in accordance with
a clinical model comparison program;
[0012] FIG. 9 is a view showing a mathematical model of a model ID
"002" in FIGS. 2 and 3, that is constructed in step S1 of FIG.
8;
[0013] FIG. 10 is a view schematically showing an analysis result
calculation step based on a mathematical model before update
corresponding to the model ID "2" in FIG. 3 using treatment result
information of a patient ID "1" of a treatment ID "1" in FIG.
5;
[0014] FIG. 11 is a view schematically showing an analysis result
calculation step based on a mathematical model before update
corresponding to a model ID "3" in FIG. 3 using the treatment
result information of the patient ID "1" of the treatment ID "1" in
FIG. 5;
[0015] FIG. 12 is a table showing a result of calculating accuracy
indices in step S3 of FIG. 8;
[0016] FIG. 13 is a table showing a comparison index (a certainty
factor difference and the degree of a clinical change) calculated
in step S3 of FIG. 8;
[0017] FIG. 14 is a view showing an example of a verification
screen that is generated in step S6 of FIG. 8 and indicates the
certainty factor difference and the degree of the clinical change
between mathematical models before and after update for each of
seven patients in descending order of a reference count;
[0018] FIG. 15 is a view showing a verification screen on which
detailed information of a patient N in FIG. 14 is superimposed;
[0019] FIG. 16 is a view showing a verification screen in a list
format generated in step S6 of FIG. 8; and
[0020] FIG. 17 is a view showing a verification screen that is
generated in step S6 of FIG. 8 from a viewpoint different from
those in FIGS. 14 and 16.
DETAILED DESCRIPTION
[0021] A radiotherapy planning apparatus according to this
embodiment includes processing circuitry and a display. The
processing circuitry calculates, by applying patient information to
each of a plurality of analysis models relating to clinical
practice, analysis results based on the analysis models. The
processing circuitry compares each of the analysis results with an
actual clinical result relating to a comparison target patient, and
generates evaluation information to evaluate a change between the
analysis models. The display displays the evaluation
information.
[0022] An analysis model according to the embodiment is a
mathematical model which mathematically calculates the clinical
result information based on patient information relating to the
clinical practice of the patient. The mathematical model according
to the embodiment includes a prediction model that predicts a
future result based on actual patient information. The mathematical
model according to the embodiment includes not only the prediction
model that predicts a future result, but also a model that predicts
(or analyzes) information which was not, is not or will not be
measured. The clinical practice in this embodiment is medical
practice including one of an examination, a diagnosis, a treatment
and a follow-up.
[0023] The examination according to the embodiment indicates an
examination using a blood test apparatus, an immunological test
apparatus, and bio-instrumentation equipment, such as an
electrocardiograph and a hemomanometer. For example, the
mathematical model relating to the examination determines whether
or not there is an abnormality in cardiac functions, using a
electrocardiographic waveform and measurement results obtained by
other bio-instrumentation equipment (for example, a blood-pressure
value and blood component values), as input patient information.
Even if an abnormality in the electrocardiographic waveform is not
detected within an electrocardiographic waveform measurement
period, the mathematical model according to the embodiment can
predict a possibility of an abnormality in the electrocardiographic
waveform after the electrocardiographic waveform measurement
period.
[0024] The diagnosis according to the embodiment indicates a
diagnosis using an X-ray computed tomography machine or a medical
diagnostic imaging apparatus, such as an X-ray diagnosis apparatus,
a magnetic resonance imaging apparatus, an ultrasonic diagnosis
apparatus, or a nuclear medicine diagnosis apparatus. For example,
the mathematical model relating to a diagnosis uses a medical image
as input patient information, and outputs an image diagnosis
result.
[0025] The treatment according to the embodiment indicates
radiotherapy using a radiotherapy apparatus. A mathematical model
relating to the radiotherapy will be described in detail later.
[0026] The follow-up according to the embodiment indicates a
follow-up of a treatment portion after medical practice, such as an
examination, a diagnosis, and a treatment, is provided there. For
example, the mathematical model relating to the follow-up uses a
medical image as input patient information, and outputs
presence/absence of a relapse of a pathological abnormality, such
as a cancer.
[0027] The mathematical models according to the embodiment will be
described below referring to prediction models for predicting
treatment progress of radiotherapy.
[0028] A radiotherapy planning apparatus and a clinical model
comparison method according to this embodiment will be described
below with reference to the accompanying drawings.
[0029] FIG. 1 is a block diagram showing the arrangement of a
radiotherapy planning apparatus 100 according to this embodiment.
As shown in FIG. 1, the radiotherapy planning apparatus 100
according to this embodiment includes processing circuitry 1, a
mathematical model database 2, a patient information database 3, a
reference tendency information database 4, image processing
circuitry 5, a communication interface 6, a display 7, an input
interface 8, and main storage circuitry 9. The processing circuitry
1, the mathematical model database 2, the patient information
database 3, the reference tendency information database 4, the
image processing circuitry 5, the communication interfaces 6, the
display 7, the input interface 8, and the main storage circuitry 9
are communicably connected via a bus.
[0030] The processing circuitry 1 includes, as hardware resources,
processors such as a CPU (Central Processing Unit) and a GPU
(Graphics Processing Unit), and memories such as a ROM (Read Only
Memory) and a RAM (Random Access Memory). The processing circuitry
1 implements a mathematical model acquisition function 11, an
analysis result calculation function 12, an index calculation
function 13, a comparison target setting function 14, a
verification screen generation function 15, and a reference
tendency information recording function 16 by executing a clinical
model comparison program stored in the main storage circuitry 9.
The mathematical model acquisition function 11, the analysis result
calculation function 12, the index calculation function 13, the
comparison target setting function 14, the verification screen
generation function 15, and the reference tendency information
recording function 16 correspond to modules of the clinical model
comparison program, respectively. The processing circuitry 1 also
implements a mathematical model update function 17 by executing a
mathematical model update program stored in the main storage
circuitry 9. In addition, the processing circuitry 1 implements a
radiotherapy planning function 18 by executing a radiotherapy
planning program stored in the main storage circuitry 9. Note that
the processing circuitry 1 may be implemented by an ASIC
(Application Specific Integrated Circuit), an FPGA (Field
Programmable Gate Array), a CPLD (Complex Programmable Logic
Device), or an SPLD (Simple Programmable Logic Device) capable of
implementing the above functions.
[0031] In the mathematical model acquisition function 11, the
processing circuitry 1 acquires, from the mathematical model
database 2, a pair of mathematical models to be compared. The
mathematical models according to this embodiment are mathematical
models for predicting treatment progress of radiotherapy. The
treatment progress to be predicted specifically includes the
response of a tumor after radiation irradiation and the
presence/absence of a side effect. The response of the tumor is
indicated by, for example, the probability of cure of the tumor.
The presence/absence of the side effect is indicated by, for
example, the presence/absence of weight loss. The pair of
mathematical models to be compared may be any mathematical models
that can be compared. One of the mathematical models of the pair
will be referred to as the first mathematical model hereinafter,
and the other will be referred to as the second mathematical model
hereinafter.
[0032] The mathematical model database 2 is a database for storing
a plurality of mathematical models. More specifically, the
mathematical model database 2 stores basic information (to be
referred to as basic model information hereinafter) of each
mathematical model, and detailed information (to be referred to as
detailed model information hereinafter) of each mathematical
model.
[0033] FIG. 2 is a table (to be referred to as a basic model
information table hereinafter) of the basic model information. As
shown in FIG. 2, the basic model information includes items of a
model ID, algorithm, model name, current version, and old version.
The model ID is an identifier assigned to the corresponding
mathematical model. The algorithm is a kind of algorithm of the
corresponding mathematical model. Any algorithm such as a decision
tree or a regression tree is applicable as a mathematical model
verification algorithm according to this embodiment. Another
algorithm can be, for example, logistic regression or a neural
network. The model name is the name of the corresponding
mathematical model, or the name of a treatment progress item to be
analyzed. Examples of the model name are weight loss for predicting
the presence/absence of weight loss of a patient after radiation
irradiation and tumor response for predicting the degree of
reduction of a tumor after radiation irradiation. The current
version is the current version of the corresponding mathematical
model. The old version is the version of the corresponding
mathematical model before update. For example, for a mathematical
model of a model ID "002", the algorithm is "decision tree", the
model name is "weight loss", the current version is "2.0", and the
old version is "1.0". Note that a mathematical model having "-" as
the old version such as a mathematical model of a model ID "001" or
"004" indicates a mathematical model having a current version
"1.0", that is, a mathematical model of an initial version. The
basic model information table is managed in the mathematical model
database 2.
[0034] FIG. 3 is a table (to be referred to as a detailed model
information table hereinafter) of the detailed model information.
As shown in FIG. 3, the detailed model information includes items
of a parameter ID, model ID, parameter name, operator, value, false
branch, true branch, the certainty factor of the true branch, and
the certainty factor of the false branch. Note that the detailed
model information shown in FIG. 3 is an example of detailed
information of a mathematical model using the decision tree. Note
that the detailed model information table is applicable not only to
a mathematical model using the decision tree but also to a
mathematical model using the logistic regression or neural network,
and has a table structure corresponding to the mathematical
model.
[0035] The parameter ID is an identifier assigned to each of
parameters forming the corresponding mathematical model. The
parameter name is the name of the corresponding parameter. The
operator is an operator forming the conditional expression of a
branch, and defines the relationship between a parameter value and
a condition value. More specifically, examples of the operator are
"<" indicating that the parameter value is smaller than the
condition value, ">" indicating that the parameter value is
larger than the condition value, and "=" indicating that the
parameter value is equal to the condition value. The condition
value is a value forming the conditional expression of the branch,
and is to be compared with the parameter value. The false branch is
a next node to which the process transits when the conditional
expression of the branch is not satisfied. The next node indicates
a next branch or a false leaf that outputs an analysis result. The
true branch is a next node to which the process transits when the
conditional expression of the branch is satisfied. The next node
indicates a next branch or a true leaf that outputs an analysis
result. The certainty factor of the false branch indicates a
certainty factor for the analysis result of the false branch. The
certainty factor of the true branch indicates a certainty factor
for the analysis result of the true branch. In this embodiment, the
certainty factors are a kind of analysis result. Note that the
certainty factors are not essential items. If, for example, the
analysis result is a numerical value such as the probability of
cure, the certainty factors need not be set.
[0036] For example, assume that for a parameter of a parameter ID
"00102", the model ID is "001", the parameter name is "ICD9", the
operator is "=", the condition value is "161", the false branch is
"absence of weight loss", the true branch is "presence of weight
loss", the certainty factor of the false branch is "60%", and the
certainty factor of the true branch is "80%". Note that a parameter
having "-" as the certainty factor of the true branch such as a
parameter of a parameter ID "00101" or "00103" indicates that it is
not at a stage where an analysis result can be calculated since
there exists a next branch. The detailed model information table is
managed in the mathematical model database 2.
[0037] As shown in FIG. 2, the mathematical model according to this
embodiment can use various algorithms such as the decision tree and
regression tree. For example, if the algorithm is the decision
tree, each mathematical model is formed by a plurality of
conditional expressions connected in the form of a tree diagram, as
shown in FIG. 3. Each conditional expression is formed by the
parameter, operator, and condition value. Each conditional
expression defines the relationship that is described by the
operator and is to be satisfied between the parameter value and the
condition value to transit to the true branch. If the conditional
expression is satisfied, the process transits to the true branch;
otherwise, the process transits to the false branch.
[0038] In the analysis result calculation function 12, the
processing circuitry 1 applies the first mathematical model and the
second mathematical model to each of pieces of patient information
of a plurality of patients, thereby calculating the first analysis
result based on the first mathematical model and the second
analysis result based on the second mathematical model for each of
the plurality of patients. The patient information is stored in the
patient information database 3.
[0039] The patient information database 3 is a database for storing
pieces of patient information of a plurality of patients. The
patient information includes basic patient information and
treatment result information. The basic patient information is
basic information for specifying a patient. The treatment result
information is information about the result of a treatment actually
applied to the patient.
[0040] FIG. 4 is a table (to be referred to as a basic patient
information table hereinafter) of the basic patient information. As
shown in FIG. 4, the basic patient information includes items of a
patient ID, patient name, sex, and age. The patient ID is an
identifier assigned to the corresponding patient. The patient name
is the name of the corresponding patient. The sex is the sex of the
corresponding patient. The age is the age of the corresponding
patient. For example, for a patient of a patient ID "1", the
patient name is "X", the sex is "male", and the age is "50". The
basic patient information table is managed in the patient
information database 3.
[0041] FIG. 5 is a table (to be referred to as a treatment result
information table) of the treatment result information. As shown in
FIG. 5, the treatment result information includes items of a
treatment ID, patient ID, ICD9, LarynxX75, ParotidX89, ParotidX70,
and the presence/absence of weight loss. The treatment ID is an
identifier assigned to a treatment applied to the corresponding
patient. The patient ID is an identifier assigned to the
corresponding patient. ICD9 is one of parameters indicating the
disease name and diagnosis result of the corresponding patient.
Each of LarynxX75, ParotidX89, and ParotidX70 is one of irradiation
parameters in radiotherapy. The presence/absence of weight loss is
one of parameters for evaluating the progress of the treatment. The
items of ICD9, LarynxX75, ParotidX89, ParotidX70, and the
presence/absence of weight loss are associated with a treatment
result actually measured after the treatment. The treatment result
information table is managed in the patient information database
3.
[0042] In the index calculation function 13, for each of the
plurality of patients, the processing circuitry 1 calculates the
first accuracy index indicating the accuracy of the first analysis
result based on the actual treatment result and the first analysis
result, calculates the second accuracy index indicating the
accuracy of the second analysis result based on the actual
treatment result and the second analysis result, and calculates a
comparison index indicating comparison between the first and second
analysis results. The treatment result information stored in the
patient information database 3 is used as the actual treatment
result. The first and second accuracy indices are the same kind of
accuracy indices to evaluate the first and second analysis
results.
[0043] In the comparison target setting function 14, the processing
circuitry 1 sets comparison target patients from the plurality of
patients. The comparison target patients may be set based on
reference tendency information (to be described later), or
arbitrarily set by the user via the input interface 8.
[0044] In the reference tendency information recording function 16,
the processing circuitry 1 records reference tendency information
in the reference tendency information database 4. The reference
tendency information is information about a tendency of reference
to medical information such as patient information and medical
images by a medical staff such as a doctor or technician in a usual
medical treatment process (workflow) of radiotherapy. The reference
tendency information is recorded for each subject (to be referred
to as a reference subject hereinafter), such as each department or
occupation, that refers to medical information, each object (to be
referred to as a reference object hereinafter), such as each
patient or treatment portion, of medical information, each period
(to be referred to as a reference period hereinafter) during which
medical information is referred to, or each treatment apparatus (to
be referred to as a reference treatment apparatus hereinafter) used
for a treatment associated with medical information. Note that
examples of the occupation as the reference subject are a doctor,
technician, and nurse. Examples of the treatment apparatus are an
IGRT (Image Guided Radiotherapy) apparatus and an IMRT (Intensity
Modulated Radiation Therapy) apparatus. The treatment apparatus is
not limited to a large-scale apparatus, and may be a small device
used for brachytherapy.
[0045] The reference tendency information database 4 is a database
for storing the reference tendency information. An example of the
reference tendency information is a reference count for each
reference subject, each reference object, each reference period, or
each reference treatment apparatus.
[0046] FIG. 6 is a table (to be referred to as a reference tendency
information table hereinafter) of the reference tendency
information for each patient. As shown in FIG. 6, the reference
tendency information for each patient includes items of a clinician
ID, patient ID, and reference count. The clinician ID is an
identifier assigned to a clinician as a reference subject in charge
of the corresponding patient. The patient ID is an identifier
assigned to the corresponding patient. The reference count is the
number of times the clinician refers to medical information of the
corresponding patient. For example, for a clinician of a clinician
ID "1001", the reference count for medical information of the
patient of the patient ID "1" is "80".
[0047] FIG. 7 is a table showing a reference tendency information
table for each treatment portion. As shown in FIG. 7, the reference
tendency information for each treatment portion includes items of a
clinician ID, treatment portion, and reference count. The clinician
ID is an identifier assigned to a clinician in charge of a
treatment for the treatment portion as a reference object. The
treatment portion is the name of the treatment portion. The
reference count is the number of times the clinician refers to
medical information about the treatment portion. For example, for a
clinician of a clinician ID "1001", the reference count for medical
information of a treatment portion "head and neck" is "80".
[0048] In the verification screen generation function 15, the
processing circuitry 1 generates evaluation information to evaluate
a clinical change between the first and second mathematical models.
The evaluation information indicates, for example, a degree of
improvement or worsening of the second mathematical model with
reference to the first mathematical model. As another example, the
evaluation information may indicate determination whether the
second mathematical model is improved or worsened relative to the
first mathematical model. More specifically, in the verification
screen generation function 15, the processing circuitry 1
calculates a clinical change between the first and second
prediction results with reference to the actual treatment result
for each of the comparison target patients set by the verification
target setting function 14. The processing circuitry 1 generates a
verification screen showing the tendency of the clinical change.
More specifically, the processing circuitry 1 generates a
verification screen schematically showing at least one of the first
and second accuracy indices or comparison index with respect to the
comparison target patient.
[0049] In the mathematical model update function 17, the processing
circuitry 1 updates the mathematical model. Update of the
mathematical model indicates change, addition, and deletion of
intrinsic parameters forming the mathematical model. The intrinsic
parameters according to this embodiment correspond to the values of
the items of the detailed information forming the mathematical
model. If, for example, the algorithm is the decision tree, update
of the mathematical model indicates change, addition, and deletion
of at least one of the operator, condition value, or branch
destination, which form the mathematical model.
[0050] In the radiotherapy planning function 18, the processing
circuitry 1 creates a treatment plan for the corresponding patient
based on a treatment plan image and like generated by a medical
image diagnostic apparatus and the like. The items of the treatment
plan include, for example, a treatment portion, the dose
distribution of radiation, a radiation irradiation method, and a
radiotherapy condition. Information about the treatment plan is
transmitted to a radiotherapy apparatus. The radiotherapy apparatus
performs radiation irradiation in accordance with the treatment
plan to kill or reduce a tumor of the patient.
[0051] The image processing circuitry 5 includes, as hardware
resources, processors such as a CPU and a GPU, and memories such as
a ROM and a RAM. The image processing circuitry 5 performs various
image processes for the treatment plan image. For example, the
image processing circuitry 5 generates a 2D medical image for
display by performing, for a 3D treatment plan image, 3D image
processing such as volume rendering, surface volume rendering,
image value projection processing, MPR (Multi-Planer
Reconstruction) processing, and CPR (Curved MPR) processing. Note
that the image processing circuitry 5 may be implemented by an
ASIC, FPGA, CPLD, or SPLD capable of implementing the above image
processes.
[0052] The communication interface 6 includes a communication
interface for performing, via wired or wireless connection (not
shown), data communication with a radiotherapy apparatus, PACS
(Picture Archiving and Communication System), HIS (Hospital
Information System), RIS (Radiology Information System), OIS
(Oncology Information System), or the like.
[0053] The display 7 displays various kinds of information such as
the verification screen generated by the verification screen
generation function 15. More specifically, for example, a CRT
display, liquid crystal display, organic EL display, LED display,
plasma display, or any other display known in this technical field
is appropriately usable as the display 7.
[0054] The input interface 8 specifically includes an input device
and input interface circuitry. The input device accepts various
commands from the user. A keyboard, a mouse, various switches, and
the like are usable as the input device. The input interface
circuitry supplies, to the processing circuitry 1, via the bus, an
output signal from the input device.
[0055] The main storage circuitry 9 is a storage device such as an
HDD (Hard Disk Drive), SSD (Solid State Drive), or integrated
circuit storage device for storing various kinds of information.
For example, the main storage circuitry 9 stores the clinical model
comparison program, model update program, and radiotherapy planning
program. The main storage circuitry 9 as hardware may be a driving
device that reads/writes various kinds of information from/in a
portable storage medium such as a CD-ROM drive, DVD drive, or flash
memory.
[0056] An example of the operation of the radiotherapy planning
apparatus 100 by executing the clinical model comparison program
according to this embodiment will be described next. The
radiotherapy planning apparatus 100 executes the clinical model
comparison program for comparing and verifying two mathematical
models of the same kind. For example, mathematical models before
and after update by the mathematical model update function 17 are
set as the two mathematical models of the same kind to be compared
and verified. The mathematical models of the same kind indicate
mathematical models whose algorithms are the same but whose
intrinsic parameters are different. For example, if the algorithm
is the decision tree, the mathematical models of the same kind
indicate mathematical models having different operators, condition
values, and branch destinations. The mathematical models of the
same kind may be mathematical models whose algorithms are different
but whose analysis targets are the same. For example, a
mathematical model for predicting weight loss by the decision tree
and a mathematical model for predicting weight loss by the
regression tree can be comparison targets as the mathematical
models of the same kind.
[0057] The two mathematical models to be compared according to this
embodiment are not limited to the mathematical models before and
after update. If two mathematical model use subjects such as
hospitals, departments, or medical staffs use different intrinsic
parameters, mathematical models for the respective use subjects are
set as the two mathematical models of the same kind to be compared.
For example, a mathematical model used in hospital A and a
mathematical model used in hospital B may be set as mathematical
models to be compared.
[0058] FIG. 8 is a flowchart illustrating the procedure of
mathematical model comparison executed by the processing circuitry
1 in accordance with the clinical model comparison program. The
processing circuitry 1 starts a mathematical model comparison when
a start instruction is automatically input or input by the user via
the input interface 8 after completion of update of a mathematical
model by the mathematical model update function 17. Note that if
update of a mathematical model is complete, the mathematical model
update function 17 supplies an update completion notification to
the mathematical model acquisition function 11. Note that the
update completion notification may include information of the model
ID of the mathematical model for which update is complete.
[0059] As shown in FIG. 8, the processing circuitry 1 executes the
mathematical model acquisition function 11 (step S1). In step S1,
the processing circuitry 1 acquires mathematical models before and
after update. The processing in step S1 will be described in detail
below.
[0060] In step S1, the processing circuitry 1 acquires detailed
model information of a mathematical model after update from the
mathematical model database 2. More specifically, the processing
circuitry 1 searches the detailed model information table shown in
FIG. 3 using, as a search keyword, the model ID of the mathematical
model after update, and specifies the old version number of a
mathematical model before update. For example, if the model ID is
"003", pieces of detailed model information of parameter IDs
"00105" and "00106" are specified.
[0061] Next, the processing circuitry 1 acquires detailed
information of the mathematical model before update from the
mathematical model database 2. More specifically, the processing
circuitry 1 searches the basic model information table shown in
FIG. 2 using, as a search keyword, the model ID of the mathematical
model after update, and specifies the old version number and model
ID of the mathematical model before update. For example, if the
model ID is "003", an old version number "2.0" and a model ID "002"
are specified. Next, the processing circuitry 1 searches the
detailed model information table shown in FIG. 3 using, as a search
keyword, the model ID of the mathematical model before update, and
specifies detailed information of the mathematical model before
update. If the model ID is "002", pieces of detailed information of
parameter IDs "00103" and "00104" are specified. Note that, for
example, two mathematical models having the same old version number
may be specified as mathematical models before update. For example,
if the mathematical model of the current version "2.1" and the
mathematical model of the current version "3.0" have an old version
number "2.0", the mathematical model of the current version "2.1"
is specified as a mathematical model before update, and the
mathematical model of the current version "3.0" is specified as a
mathematical model after update.
[0062] The processing circuitry 1 reproduces a mathematical model
before update based on the acquired detailed information of the
mathematical model before update, and constructs a mathematical
model after update based on the acquired detailed information of
the mathematical model after update.
[0063] More specifically, an example of constructing the
mathematical model of the model ID "002" as a mathematical model
before update will be described. FIG. 9 is a view showing the
mathematical model of the model ID "002" shown in FIGS. 2 and 3.
The processing circuitry 1 searches the detailed information table
shown in FIG. 3 using the model ID "002" as a search keyword, and
specifies detailed information of the mathematical model of the
model ID "002". For example, in FIG. 3, the pieces of detailed
information of the parameter IDs "00103" and "00104" are
specified.
[0064] The processing circuitry 1 specifies, as the parameter ID of
the root node of the decision tree, a parameter ID included in none
of the false branches and true branches from the plurality of
parameter IDs of the specified mathematical model before update.
For example, in the above example, the parameter ID "00104" of the
parameter IDs "00103" and "00104" is included in the true branch of
the parameter ID "00103" and thus does not correspond to the root
node. The parameter ID "00103" is included in none of the true
branches and false branches of the parameter IDs "00103" and
"00104", and is thus specified as the parameter ID of the root
node.
[0065] The processing circuitry 1 acquires the parameter name,
operator, and condition value of the specified root node, and
decides a conditional expression for the root node of the decision
tree based on the acquired parameter name, operator, and condition
value. For example, if the parameter ID of the root node is
"00103", the parameter name "ParotidX70", operator ">", and
value "1500" are specified, and thus "ParotidX70>1500" is
decided as a conditional expression.
[0066] The processing circuitry 1 specifies the values of the false
and true branches of the specified root node. The processing
circuitry 1 determines the types of the specified values of the
false and true branches. If the value of the false branch is an
analysis result, the processing circuitry 1 sets the false branch
as an end terminal (false leaf), and sets the analysis result as an
output from the false branch. At this time, if the certainty factor
of the false branch is input, the processing circuitry 1 also sets
the certainty factor of the false branch as the analysis result of
the false branch. Similarly, with respect to the true branch, the
analysis result and the certainty factor of the true branch are set
for the end terminal (true leaf) of the true branch. If the
specified value is a parameter ID, the processing circuitry 1 sets
the intrinsic parameters of the parameter ID in the next branch.
With respect to the set next branch, the same step as that
described above is executed to decide a conditional expression and
specify the values of the false and true branches. For example, if
the parameter ID of the root node is "00103", "absence of weight
loss" as the analysis result and "70%" as the certainty factor of
the false branch are set as an output for the false branch, and the
next branch "00104" is set for the true branch.
[0067] The processing circuitry 1 executes the above processing
until conditional expressions are decided and the values of the
false and true branches are specified for all the branches. If the
above processing is performed for all the branches, construction of
the mathematical model ends. Note that a mathematical model after
update can be constructed by the same process as that for the
mathematical model before update.
[0068] After step S1 is performed, the processing circuitry 1
executes the analysis result calculation function 12 (step S2). In
step S2, the processing circuitry 1 calculates an analysis result
(to be referred to as a pre-update analysis result hereinafter)
based on the mathematical model before update and an analysis
result (to be referred to as a post-update analysis result
hereinafter) based on the mathematical model after update using
each of the pieces of patient information of the plurality of
patients as calculation targets, that have been acquired from the
patient information database 3. As the plurality of patients as
calculation targets, all the patients stored in the patient
information database 3 or a plurality of patients assigned to the
user may be set. Calculation of analysis results will be described
in detail below.
[0069] First, the processing circuitry 1 acquires a patient list
from the patient information database 3. The patient list is, for
example, the treatment result information table stored in the
patient information database 3. Next, the processing circuitry 1
extracts treatment result information of an arbitrary patient from
the patient list, and acquires, from the extracted treatment result
information, the actual measured values of the parameters of the
patient used by the mathematical models before and after update.
For example, since the mathematical model of the model ID "002"
uses ICD9 and ParotidX70, an actual measured value "170" of ICD9,
an actual measured value "1600" of ParotidX70, and the like are
acquired for a record of the patient ID "1" of a treatment ID "1"
in FIG. 5. The processing circuitry 1 calculates the analysis
results of the mathematical models based on the acquired actual
measured values of the parameters.
[0070] FIG. 10 is a view schematically showing the analysis result
calculation step based on the mathematical model before update
corresponding to the model ID "2" shown in FIG. 3 using the
treatment result information of the patient ID "1" of the treatment
ID "1" shown in FIG. 5. FIG. 11 is a view schematically showing a
step of calculating a pre-update analysis result corresponding to a
model ID "3" shown in FIG. 3 using the treatment result information
of the patient ID "1" of the treatment ID "1" shown in FIG. 5. As
shown in FIG. 10, for the mathematical model before update, it is
determined whether Parotidx70 is larger than 1500. As shown in FIG.
5, for the patient ID "1" of the treatment ID "1", Parotidx70 is
"1600". Since, therefore, Parotidx70 is larger than 1500, the
process transits to the true branch. As shown in FIG. 10, in the
next true branch, it is determined whether ICD9 is equal to 170. As
shown in FIG. 5, for the patient ID "1" of the treatment ID "1",
ICD9 is "170". Since, therefore, ICD9 is equal to 170, the process
transits to the true branch. Consequently, the pre-update analysis
result is "presence of weight loss" and the certainty factor is
"80%".
[0071] For the mathematical model after update, as shown in FIG.
11, it is determined whether ParotidX70 is larger than 1400. As
shown in FIG. 5, for the patient ID "1" of the treatment ID "1",
Parotidx70 is "1600". Since, therefore, Parotidx70 is larger than
1400, the process transits to the true branch. As shown in FIG. 11,
in the next true branch, it is determined whether ICD9 is equal to
180. As shown in FIG. 5, for the patient ID "1" of the treatment ID
"1", ICD9 is "170". Since, therefore, ICD9 is not equal to 180, the
process transits to the false branch. Consequently, the post-update
analysis result is "absence of weight loss" and the certainty
factor is "60%".
[0072] After step S2 is performed, the processing circuitry 1
executes the index calculation function 13 (step S3). In step S3,
the processing circuitry 1 calculates an accuracy index based on
the pre-update analysis result, an accuracy index based on the
post-update analysis result, and a comparison index indicating
comparison between the accuracy index based on the mathematical
model before update and that based on the mathematical model after
update.
[0073] More specifically, the processing circuitry 1 acquires the
information and analysis result of the mathematical model before
update, and the information and analysis result of the mathematical
model after update, which have been calculated by the analysis
result calculation function 12, and calculation target patient
information. The processing circuitry 1 acquires actual treatment
result information of the calculation target patient information
from the patient information database 3. For example, for the
patient of the patient ID "1" of the treatment ID "1" shown in FIG.
5, the treatment result "absence of weight loss" is acquired. The
processing circuitry 1 compares the analysis result with the
treatment result to calculate an accuracy index indicating the
accuracy of the prediction. The accuracy index is calculated for
each of the mathematical models before and after update.
[0074] FIG. 12 is a table showing accuracy index calculation
results. As shown in FIG. 12, the accuracy indices are classified
into, for example, a true positive, a false positive, a true
negative, and a false negative. The true positive is calculated
when the analysis result is "presence of weight loss" and the
treatment result is "presence of weight loss". The false positive
is calculated when the analysis result is "presence of weight loss"
and the treatment result is "absence of weight loss". The true
negative is calculated when the analysis result is "absence of
weight loss" and the treatment result is "absence of weight loss".
The false negative is calculated when the analysis result is
"absence of weight loss" and the treatment result is "presence of
weight loss". For example, for the patient ID "1", the analysis
result of the mathematical model of the model ID "002" is "presence
of weight loss" but the treatment result is "absence of weight
loss". Therefore, the accuracy index is the "false positive". Note
that the accuracy index is not limited to them, and any index may
be used as long as it can indicate the degree of matching between
the analysis result and the treatment result.
[0075] The processing circuitry 1 calculates a comparison index
indicating comparison between the pre-update analysis result and
the post-update analysis result. The comparison index indicates a
tendency of a change between the degrees of matching of the
pre-update analysis result and post-update analysis result with
respect to the actual treatment result when the mathematical model
before update is updated to the mathematical model after
update.
[0076] FIG. 13 is a table showing the comparison index. As shown in
FIG. 13, the processing circuitry 1 calculates a degree of a
clinical change and a certainty factor difference as the comparison
index. The definitions of the degree of the clinical change and the
certainty factor difference are different between mathematical
models for predicting the presence/absence of a side effect and
mathematical models for predicting the numerical values of the
treatment progress parameters. The degree of the clinical change
and the certainty factor difference for the mathematical models for
predicting the presence/absence of the side effect will be
described.
[0077] The degree of the clinical change indicates the
classification of the tendency of the clinical change of the
post-update analysis result with respect to the pre-update analysis
result. More specifically, the degree of the clinical change is
decided based on whether the post-update analysis result has been
clinically improved or worsened, or has not been changed with
respect to the pre-update analysis result. The certainty factor
difference is defined as the difference between the certainty
factor of the pre-update analysis result and that of the
post-update analysis result. More specifically, the certainty
factor difference is defined as a value (subtraction value)
obtained by subtracting the certainty factor of the pre-update
analysis result from that of the post-update analysis result. The
degree of the clinical change is classified into "improvement" when
the subtraction value is "+", "worsening" when the subtraction
value is "-", or "no change" when the subtraction value is 0.
[0078] The processing circuitry 1 calculates the certainty factor
difference based on the actual treatment result, the pre-update
analysis result, and the post-update analysis result. More
specifically, for each of the mathematical models before and after
updates, the processing circuitry 1 determines whether the actual
treatment result matches the analysis result. If the actual
treatment result matches the analysis result, the processing
circuitry 1 sets the sign of the certainty factor to "+";
otherwise, the processing circuitry 1 sets the sign of the
certainty factor to "-". Then, the processing circuitry 1
calculates the certainty factor difference by subtracting the
signed certainty factor of the pre-update analysis result from that
of the post-update analysis result. The processing circuitry 1
specifies the sign and value of the certainty factor difference. If
the sign of the certainty factor difference is "-", the degree of
the clinical change is set to "worsening". If the sign of the
certainty factor difference is "+", the degree of the clinical
change is set to "improvement". If the certainty factor difference
is 0, the degree of the clinical change is set to "no change".
[0079] For example, if, like a patient "A", the treatment result is
"presence of weight loss", the pre-update analysis result is
"presence of weight loss (certainty factor: 80%)", and the
post-update analysis result is "absence of weight loss (certainty
factor: 40%)", the certainty factor difference is (-40)-80=-120,
and the degree of the clinical change is "worsening". If, like a
patient "D", the treatment result is "absence of weight loss", the
pre-update analysis result is "absence of weight loss (certainty
factor: 70%)", and the post-update analysis result is "absence of
weight loss (certainty factor: 80%)", the certainty factor
difference is (80)-70=10, and the degree of the clinical change is
"improvement".
[0080] The degree of the clinical change and the certainty factor
difference for the mathematical models for predicting the numerical
values of the treatment progress parameters will be described next.
As the mathematical models for predicting the numerical values of
the treatment progress parameters, the mathematical models for the
probability of cure of the tumor are exemplified. The degree of the
clinical change is decided based on whether the post-update
analysis result has been clinically improved or worsened, or has
not been changed with respect to the pre-update prediction result.
The certainty factor difference is defined as the difference
between the pre-update analysis result (numerical value) and the
post-update analysis result (numerical value). The degree of the
clinical change is classified into "improvement" when the
subtraction value is "+", "worsening" when the subtraction value is
"-", or "no change" when the subtraction value is 0.
[0081] The processing circuitry 1 calculates the certainty factor
difference based on the actual treatment result, the pre-update
analysis result, and the post-update analysis result. If the
treatment result is "cured", the sign of the probability of cure as
the analysis result is set to "+". If the treatment result is "not
cured", the sign of the probability of cure as the analysis result
is set to "-". For example, if the treatment result is "cured", the
pre-update analysis result is "probability of cure: 10%", and the
post-update analysis result is "probability of cure: 95%", the
certainty factor difference is (95)-10=85, and thus the degree of
the clinical change is "improvement". If the treatment result is
"not cured", the pre-update analysis result is "probability of
cure: 80%", and the post-update analysis result is "probability of
cure: 20%", the certainty factor difference is (-20)-(-80)=60, and
thus the degree of clinical change is "improvement".
[0082] After step S3 is performed, the processing circuitry 1
executes the comparison target setting function 14 (step S4). In
step S4, the processing circuitry 1 sets comparison target patients
from the plurality of patients based on the reference tendency
information stored in the reference tendency information database
4.
[0083] If, for example, the reference count for each patient in
FIG. 6 is stored as the reference tendency information, the
processing circuitry 1 sets, as comparison targets, 10 patients in
descending order of the reference count. Note that the comparison
targets are not limited to the 10 patients from the top, and
patients of any ordinal numbers from the top may be set. The
present invention is not limited to a predetermined number of
patients from the top, and the predetermined number of patients
from the bottom may be set.
[0084] As described above, the reference tendency information is
recorded by the reference tendency information recording function
16 of the processing circuitry 1. More specifically, if a reference
subject such as a doctor uses the radiotherapy apparatus, PACS,
HIS, RIS, or OIS, the processing circuitry 1 acquires the ID of the
reference subject. The processing circuitry 1 detects an event (to
be referred to as a reference event hereinafter) of referring to
(accessing) medical information of a patient by the reference
subject. For example, the processing circuitry 1 detects, as a
reference event, via the input interface 8 or the like, transition
of the screen of a user interface or an input operation for
referring to medical information. Based on the ID of the reference
subject and the ID of the patient as the reference object, the
processing circuitry 1 increases the reference count for a
combination of the reference subject and reference object.
[0085] Note that a medical treatment process of radiotherapy at the
time of reference may be included as the reference tendency
information. In this case, the processing circuitry 1 acquires the
medical treatment process of radiotherapy at the time of reference
from the radiotherapy apparatus, PACS, HIS, RIS, or OIS along with
detection of the reference event. Examples of the medical treatment
process are creation of a treatment plan, replanning of the
treatment plan, and follow-up. The processing circuitry 1 increases
the reference count in the acquired medical treatment process. In
this case, when setting comparison target patients, it is possible
to consider the reference counts for each medical treatment
process. For example, 10 patients in descending order of the
reference count at the time of follow-up can be set as comparison
target patients.
[0086] The comparison target patients are not limited to some of
the plurality of patients for which the accuracy indices have been
calculated. That is, all the plurality of patients for which the
accuracy indices have been calculated may be set as comparison
targets. In this case, the processing circuitry 1 automatically
sets, as comparison targets, all the plurality of patients for
which the accuracy indices have been calculated, without using the
reference tendency information. Alternatively, arbitrary patients
designated by the user via the input circuitry 8 among the
plurality of patients for which the accuracy indices have been
calculated may be set as comparison target patients.
[0087] After step S4 is performed, the processing circuitry 1
executes the verification screen generation function 15 (step S5).
In step S5, the processing circuitry 1 generates a verification
screen showing the tendency of the clinical change between the
pre-update analysis result and the post-update analysis result.
[0088] After step S5 is performed, the processing circuitry 1
causes the display 7 to execute display processing (step S6). In
step S6, the display 7 displays the verification screen generated
by the verification screen generation function 15.
[0089] The verification screen generation processing in step S5 and
the verification screen display processing in step S6 will be
described in detail below by exemplifying a case in which a
predetermined number of patients in descending order of the
reference count are set as comparison targets.
[0090] The processing circuitry 1 generates a verification screen
based on at least one of the pre-update analysis result, the
post-update analysis result, the accuracy index based on the
pre-update analysis result, the accuracy index based on the
post-update analysis result, or the comparison index, all of which
are associated with each comparison target patient. For example,
the processing circuitry 1 generates, as a verification screen, a
chart (graph) showing the certainty factor differences and the
degrees of the clinical changes for the mathematical models before
and after update, which are associated with the predetermined
number of patients in descending order of the reference count.
[0091] FIG. 14 is a view showing an example of a verification
screen I1 showing the certainty factor differences and the degrees
of the clinical changes for the mathematical models before and
after update, which are associated with seven patients in
descending order of the reference count. As shown in FIG. 14, the
mathematical model before update is a 2015 model for prediction of
the presence/absence of weight loss, and the mathematical model
after update is a 2016 model for prediction of the presence/absence
of weight loss. The user of mathematical model verification
processing is Dr. .largecircle..largecircle. Taro. The comparison
target patients are seven patients in descending order of the
reference count by Dr. .largecircle..largecircle. Taro.
[0092] As shown in FIG. 14, in the verification screen I1, the
ordinate is defined as the certainty factor difference and the
abscissa is defined as the reference count. The + range of the
certainty factor difference is categorized as "improvement" of the
degree of the clinical change, and the - range of the certainty
factor difference is categorized as "worsening" of the degree of
the clinical change. The verification screen I1 visually shows the
certainty factor difference and the degree of the clinical change
for each patient by the length and direction of each bar. That is,
the length of each bar represents the absolute value of the
certainty factor difference, and the direction of each bar
represents the degree of the clinical change. The display 7
displays the verification screen I1, thereby making it possible to
clearly present information indicating whether the analysis result
has been improved or worsened before and after update of the
mathematical model and information indicating the degree of
improvement or worsening. Furthermore, the bars of the patients are
arranged so that the bar closer to the origin of the graph
indicates a larger reference count. In FIG. 14, the patient C has
the largest reference count. By arraying the bars of the patients
in accordance with the reference counts, the bar of the patient
having a large reference count can be readily identified. Since the
display 7 displays comparison (for example, the certainty factor
difference and the degree of the clinical change) between the
analysis results based on the mathematical models before and after
update, and the like for each of the patients to which the user
such as a doctor often refers in the medical treatment process, the
user can readily determine the validity of the mathematical models
before and after update.
[0093] The patient name or bar of each patient included in the
verification screen I1 functions as a user interface, and is
displayed to be selectable via the input interface 8. If the user
designates a patient name or bar in the verification screen I1 via
the input circuitry 8, the display 7 may display detailed
information D1 of the patient corresponding to the designated
patient name or bar, as shown in FIG. 15. FIG. 15 is a view showing
the verification screen I1 on which detailed information D1 of a
patient N is superimposed. As shown in FIG. 15, for example, a tree
diagram of the mathematical model before update which shows a
decision path and a tree diagram of the mathematical model after
update which shows a decision path are displayed as the detailed
information D1. Note that in FIG. 15, the decision paths are
represented by thick arrows. As described above, when the display 7
displays, side by side, the tree diagrams of the mathematical
models before and after update which show the decision paths, the
user can more accurately determine the validity of the analysis
results. The type of the detailed information D1 is not limited to
the tree diagrams which show the decision paths. For example, tree
diagrams which show no decision paths, the treatment result
information of the patient, or the like may be displayed as
detailed information.
[0094] Note that the display format of comparison of the analysis
results is not limited to the graph format such as the verification
screens shown in FIGS. 14 and 15, and may be a list format.
[0095] FIG. 16 is a view showing a verification screen 12 in a list
format. As shown in FIG. 16, the mathematical models before and
after update are the mathematical models for the probability of
cure of the tumor. The user of mathematical model verification
processing is Dr. .largecircle..largecircle. Taro. The comparison
target patients are 34 patients assigned to Dr.
.largecircle..largecircle. Taro. As shown in FIG. 16, in the
verification screen 12, a list of the actual treatment results, the
probabilities of cure as the pre-update analysis results, and the
probabilities of cure as the post-update analysis results of the
respective patients is extracted for each of the degrees of the
clinical changes (improvement and worsening). For each of
"improvement" and "worsening", data are displayed from the top in
descending order of the difference (certainty factor difference)
between the probability of cure before update and that after
update. That is, the data are displayed in descending order of the
clinical change between the analysis results before and after
update.
[0096] The display order of the patients is selectable from various
viewpoints. For example, the verification screen 12 includes a
pull-down menu M1 for selecting a viewpoint for the display order.
The viewpoint for the display order is not limited to the
descending (or ascending) order of the certainty factor difference,
and may be the descending (or ascending) order of the analysis
result value or the descending order of the reference count.
[0097] As described above, in the verification screen 12,
comparison between the probability of cure based on the
mathematical model before update and that based on the mathematical
model after update is displayed for each patient to which the
reference subject such as a clinician often refers in the medical
treatment process. Therefore, even a person who is not well versed
in statistics can determine whether the mathematical models before
and after update are good or bad.
[0098] FIG. 17 is a view showing a verification screen 13 from
another viewpoint. As shown in FIG. 17, the verification screen 13
shows statistics of the tendencies of the clinical changes between
the pre-update analysis results and the post-update analysis
results with respect to the comparison target patients. For
example, the verification screen 13 shows, for each of the degrees
of the clinical changes (improvement and worsening), the numbers of
patients for respective ages for which the difference between the
pre-update analysis result and the post-update analysis result is
equal to or larger than 50%. The mathematical model is the
probability of cure of the tumor. The display 7 displays a
pull-down menu M2 capable of selecting a sort of the results from a
viewpoint other than the age. The viewpoint other than the age may
be the sex, the treatment apparatus, or any other viewpoint. The
verification screen 13 displays patients limited to those each
having a certainty factor difference of 50% or more but may display
patients each having a difference equal to or larger or smaller
than a numerical value other than 50%. The verification screen 13
displays, in a list format, the numbers of patients each having a
certainty factor difference of 50% or more but may display them in
a chart format such as a histogram or a distribution map.
[0099] The display 7 can display statistics of the tendencies of
the clinical changes between the pre-update analysis results and
the post-update analysis results with respect to the comparison
target patients. Since the user often refers to the comparison
target patients in daily medical treatment processes, he/she can
understand the statistics well.
[0100] Mathematical model comparison executed by the processing
circuitry 1 in accordance with the mathematical model comparison
program has been described.
[0101] Note that various changes can be made for the processing
procedure shown in FIG. 8. For example, the comparison target
patient setting processing in step S4 may be provided before the
analysis result. In this case, the analysis result calculation
processing in step S2 and the accuracy index calculation processing
in step S3 are performed only for the comparison target patients.
Therefore, the analysis result calculation processing in step S2
and the accuracy index calculation processing in step S3 for a
patient that is not involved in comparison between the analysis
results can be omitted.
[0102] In step S1, mathematical models are constructed based on the
basic model information and detailed model information stored in
the mathematical model database 2. However, constructed
mathematical models may be stored in the mathematical model
database 2. In this case, in step S1, it is not necessary to
perform mathematical model construction processing, and
mathematical models before and after update are read out from the
mathematical model database 2 based on the model ID or the
like.
[0103] In this embodiment, the first and second mathematical models
are compared. However, three or more mathematical models may be
compared. For example, the processing circuitry 1 compares the
mathematical model before update with mathematical model 1 after
update, and further compares the mathematical model before update
with mathematical model 2 after update by the same processing as
that in the above embodiment. The processing circuitry 1 generates
a verification screen simultaneously or parallelly showing the
comparison result of the mathematical model before update and
mathematical model 1 after update and the comparison result of the
mathematical model before update and mathematical model 2 after
update, and the display 7 displays the generated verification
screen. This enables the user to readily verify/determine which of
mathematical models 1 and 2 after update is better.
[0104] As described above, the radiotherapy planning apparatus 100
according to this embodiment includes the processing circuitry 1
and the display 7. The processing circuitry 1 calculates, by
applying patient information to each of a plurality of analysis
models relating to clinical practice, analysis results based on the
analysis models. The processing circuitry 1 compares each of the
analysis results with an actual clinical result relating to a
comparison target patient, and generates evaluation information to
evaluate a change between the analysis models. The display 7
displays the evaluation information.
[0105] In the first embodiment, the processing circuitry 1 applies
the first and second mathematical models related to the treatment
progress to each of pieces of patient information of a plurality of
patients, thereby calculating the first analysis result based on
the first mathematical model and the second analysis result based
on the second mathematical model for each of the plurality of
patients. The processing circuitry 1 sets comparison target
patients from the plurality of patients. The processing circuitry 1
generates a verification screen showing, for each of the comparison
target patients, the tendency of the clinical change between the
first and second analysis results with reference to an actual
treatment result. The display circuitry 7 displays the verification
screen.
[0106] In accordance with the above arrangement, since the
radiotherapy planning apparatus 100 according to this embodiment
displays comparison between the analysis results based on the
mathematical models before and after update for each of the
comparison target patients, the user such as a doctor can readily
and accurately determine the validity of the mathematical models
before and after update without being well versed in statistics. As
a result, it is possible to use mathematical models of high
reliability, and thus the user can appropriately make a medical
judgment for improving the QOL of each patient based on the
analysis results of the mathematical models.
[0107] Therefore, it is possible to readily verify mathematical
models related to clinical practice.
[0108] While certain embodiments have been described, these
embodiments have been presented by way of example only, and are not
intended to limit the scope of the inventions. Indeed, the novel
embodiments described herein may be embodied in a variety of other
forms; furthermore, various omissions, substitutions and changes in
the form of the embodiments described herein may be made without
departing from the spirit of the inventions. The accompanying
claims and their equivalents are intended to cover such forms or
modifications as would fall within the scope and spirit of the
inventions.
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